Event Registration - Introduction To Time Series Analysis In Python

Overview
This hands-on data science course teaches the fundamentals of time series analysis and
how to build time series models in Python. Whether you are trying to predict asset prices or
understand the effects of air pollution over time, effective time series analysis can help you.
At the end of the workshop, participants will be comfortable applying the Python
programming language to visualize and execute time series analysis to see if there is
predictive power in your data.

What This Course Offers

An overview of time series models and how to use them to solve real-world problems

Hands-on Python programming experience

Course notes, certificate of completion, and post-seminar email support for 3 months

An engaging and practical training approach with a qualified instructor with relevant

technical, business, and educational experiences

A Computer Science 101 pre-course webinar

Who Is This For
This course is relevant for individuals working with or needing to understand times series.
The most common participants are: investment professionals, traders, economists,
biologists, chemists, physicists, entrepreneurs, consultants, and technology individuals.
Cognitir’s Introduction to Data Science course or the equivalent is required.

Course and Contact Information
Course Prerequisites: Introduction to Data Science is a prerequisite. If you have not been
able to take this course with us yet, please contact us.
info@cognitir.com
+1 908 505 5991 (US); +44 75 0686 49 85 (UK)
www.cognitir.com

Course Curriculum

Overview of Time Series Analysis

What is it, wide variety of use cases, time series analysis vs. time series
forecasting, common statistical problems in time series (leptokurtic,
heteroskedasticity, serial correlation) and common tests to test for these
issues (look at error residuals)

Organizing and Visualizing Time Series Data

Exploring Your Time Series Data

Start, end, frequency, number of data points

Basic Time Series Plots

Sampling Frequency

Missing Values

How to do this in Python • with an example

Organizing and Visualizing Time Series Coding Challenge

Time Series Stationarity

Trends

Random or Not

Stationary vs. Non-Stationary

Unit/root test

Removing variability trends through logarithmic transformation

Differencing

White Noise Model

Random Walk Model

How to do this in Python • with example

Time Series Stationarity Coding Challenge

Autocorrelation and Partial Autocorrelation

Financial Time Series

Autocorrelation and Calculation

Autocorrelation Function

Partial Autocorrelation Function

How to do this in Python

Autocorrelation and Partial Autocorrelation Coding Challenge

Time Series ARIMA Models

Autocorrelation and Autoregression

Random Walk vs. AR

Autocorrelation and simple moving averages

Selecting ARIMA model parameters

ARIMA model Estimate and Forecasting

How to do this in Python

ARIMA Model Coding Challenge

Time Series Model Evaluation

Visualizing model predictions

In Sample versus Out of Sample Accuracy

Types of time series error metrics

Model residual diagnostics

Model Evaluation Coding Challenge

Final Project

Address:

MCLE New England4751, 10 Winter PlBoston, MA 02108

Description:Members: $399
Non- Members: $499

CE CreditCFA Boston has determined that this event qualifies for 8 CE credit hours under the guidelines of CFA Institute's Continuing Education Program. If you are a CFA Institute member, CE credit for your participation in this event will be automatically recorded in your CE tracking tool.

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